The Geographic Diversity of U.S. Nonmetropolitan Growth Dynamics: A Geographically Weighted Regression Approach

Spatial heterogeneity is introduced as an explanation for local-area growth mechanisms, especially employment growth. As these effects are difficult to detect using conventional regression approaches, we use Geographically Weighted Regressions (GWR) for non-metropolitan U.S. counties. We test for ge...

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Bibliographic Details
Published in:Land economics 2008-05, Vol.84 (2), p.241-266
Main Authors: Partridge, Mark D, Rickman, Dan S, Ali, Kamar, Olfert, M. Rose
Format: Article
Language:English
Subjects:
Agricultural workers
Censuses
College graduates
Comparative analysis
Economic growth models
Employment
Environment
Geographic regions
Geography
Growth rate
Human capital
Land economics
Land use
Population education
Population growth rate
Public policy
Regression analysis
Rural areas
Statistical significance
Studies
U.S.A
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Summary:Spatial heterogeneity is introduced as an explanation for local-area growth mechanisms, especially employment growth. As these effects are difficult to detect using conventional regression approaches, we use Geographically Weighted Regressions (GWR) for non-metropolitan U.S. counties. We test for geographic heterogeneity in the growth parameters and compare them to global regression estimates. The results indicate significant heterogeneity in the regression coefficients across the country, most notably for amenities and college graduate shares. Using GWR also exposes significant local variations that are masked by global estimates suggesting limitations of a one-size-fits-all approach to describe growth and to inform public policy . (JEL R11, R23)
ISSN:0023-7639
1543-8325
DOI:10.3368/le.84.2.241